We use reconstruction task to learn more meaningful features of PET exams, and outcome prediction task so that the network will focus attention on the most discriminator regions for the segmentation task so that. In our experiments, the Adam optimizer [1]} algorithm was used with a mini batch of 4 and a learning rate of 0.0001. The global loss function (loss glob) for the 4 tasks is defined by:
\(\bf {loss\_glob} = \alpha Lrecon + \beta Lseg + \omega Lclass + \lambda predict\) 
